Method and apparatus for estimating a motion parameter

ABSTRACT

A method and apparatus for estimating a motion parameter corresponding to a subject element employs one or more accelerometers operable to measure accelerations and a processing system operable to generate a motion parameter metric utilizing the acceleration measurements and estimate the motion parameter using the motion parameter metric.

RELATED APPLICATIONS

The present application claims the benefit of U.S. ProvisionalApplication No. 60/778,793, entitled “METHOD AND SYSTEM FOR QUICKDISTANCE MEASUREMENT,” filed Mar. 3, 2006, which is herein incorporatedby reference in its entirety.

BACKGROUND

1. Field

Embodiments of the present invention relate to methods and apparatusesfor estimating motion parameters. More particularly, various embodimentsof the invention provide methods and apparatuses operable to estimate amotion parameter utilizing one or more acceleration measurements.

2. Description of the Related Art

Motion parameters, such as acceleration, average velocity, stridedistance, total distance, gait efficiency, and the like, may be utilizedin the training and evaluation of athletes and animals, therehabilitation of the injured and disabled, and in various recreationalactivities. Unfortunately, motion parameters acquired usingcommonly-available pedometers are often inaccurate due to stride lengthsand other sensed attributes that vary while users move or exercise.Further, methods for compensating for changes in stride lengths oftenrely upon expensive, complex, and/or bulky equipment.

SUMMARY

The present invention is directed to methods and apparatuses operable toestimate a motion parameter utilizing one or more accelerationmeasurements. In various embodiments the present invention provides anapparatus including one or more accelerometers and a processing system.The one or more accelerometers are operable to measure accelerations andthe processing system is operable to generate a motion parameter metricutilizing the acceleration measurements and estimate the motionparameter using the motion parameter metric. The motion parameter may beestimated for each of a user's strides to accurately reflect userperformance.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not necessarily restrictive of the invention claimed. Theaccompanying drawings, which are incorporated in and constitute a partof the specification, illustrate embodiments of the invention andtogether with the general description, serve to explain the principlesof the invention.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

Various embodiments of the present invention are described in detailbelow with reference to the attached drawing figures, wherein:

FIG. 1 is a schematic diagram illustrating a user employing a sensorunit and a user interface unit configured in accordance with variousembodiments of the present invention;

FIG. 2 is a schematic diagram illustrating an exemplary orientation ofvarious sensors within or on a shoe;

FIG. 3 is a block diagram illustrating some of the components operableto be utilized by various embodiments of the present invention;

FIG. 4 is a block diagram illustrating some of the components of FIG. 3in more detail;

FIG. 5 is a block diagram illustrating an external systems unit incommunication with the sensor unit and user interface unit of FIG. 1;

FIG. 6 is a block diagram illustrating the user interface unit andsensor unit of FIG. 5 in communication with a GPS receiver;

FIG. 7 is a block diagram illustrating another configuration of the userinterface unit and GPS receiver of FIG. 5;

FIG. 8 is a block diagram illustrating another configuration of thesensor unit and GPS receiver of FIG. 5;

FIG. 9 is a block diagram illustrating another configuration of the GPSreceiver, user interface unit, and sensor unit of FIG. 5;

FIG. 10 is a schematic diagram showing the interaction of a plurality ofapparatuses configured in accordance with various embodiments of thepresent invention; and

FIG. 11 is a chart showing an exemplary correlation between a motionparameter metric and stride speed.

The drawing figures do not limit the present invention to the specificembodiments disclosed and described herein. The drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating various embodiments of the invention.

DETAILED DESCRIPTION

The following detailed description of various embodiments of theinvention references the accompanying drawings which illustrate specificembodiments in which the invention can be practiced. The embodiments areintended to describe aspects of the invention in sufficient detail toenable those skilled in the art to practice the invention. Otherembodiments can be utilized and changes can be made without departingfrom the scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense. Thescope of the present invention is defined only by the appended claims,along with the full scope of equivalents to which such claims areentitled.

Various embodiments of the present invention provide an apparatus 10operable to estimate a motion parameter based on one or moreacceleration measurements. In particular, the apparatus 10 is operableto generate a motion parameter metric that may be used to estimate themotion parameter. The motion parameter metric may be generated utilizingone or more acceleration measurements and/or other data and informationsensed or acquired by the apparatus 10.

In various embodiments, the apparatus 10 includes one or moreaccelerometers 12, a filtering element 14, and a processing system 16.The accelerometers 12, filtering element 14, and processing system 16may be integrated together or form discrete elements that may beassociated with each other. The processing system 16 is generallyoperable to generate the motion parameter metric and estimate the motionparameter using one or more acceleration measurements provided by theone or more accelerometers 12 and/or filtering element 14.

The one or more accelerometers 12 are each operable to measure anacceleration and generate an acceleration measurement corresponding tothe measured acceleration. The acceleration measurement may be embodiedas a signal operable to be utilized by the filtering element 14 and/orprocessing system 16. In some embodiments, one or more of theaccelerometers 12 may be operable to output an analog signalcorresponding to an acceleration measurement. For instance, eachaccelerometer 12 may output an analog voltage signal that isproportional to measured accelerations. In some embodiments, one or moreof the accelerometers 12 may include the ADXL321 accelerometermanufactured by ANALOG DEVICES of Norwood, Mass. However, the one ormore accelerometers 12 may include any digital and analog componentsoperable to generate a signal corresponding to a measured acceleration.Thus, in some embodiments, one or more of the accelerometers 12 areoperable to output a digital signal representing measured accelerations.

The one or more accelerometers 12 are configured to couple with orattach to a subject element S that corresponds to the motion parametersought to be estimated. In some embodiments, the subject element S maybe a portion of a human, animal, or object. For example, as shown inFIGS. 1 and 2, one or more of the accelerometers 12 may be coupled witha runner's shoe to facilitate accurate estimation of motion parameterscorresponding to the runner. In other embodiments, one or more of theaccelerometers 12 may be coupled with other portions of a human oranimal, and/or with inanimate objects such as balls, rackets, clubs,watches, clothing, bats, skis, motor vehicles, wheels, bicycles,combinations thereof, and the like, to enable motion parameters to beestimated for any subject element S.

In various embodiments, as shown in FIG. 2, the apparatus 10 may includetwo or more accelerometers 12 each operable to output a signalcorresponding to a measured acceleration. In some embodiments, theapparatus 10 includes at least two accelerometers 12 adapted to measureaccelerations in two directions separated by an angle greater than zerodegrees and each provide a signal corresponding to the measuredacceleration. Further, the apparatus 10 may include at least threeaccelerometers 12 adapted to measure accelerations in three directionseach separated by an angle greater than zero degrees and each provide asignal corresponding to the measured acceleration. However, theapparatus 10 may include any number of accelerometers 12, including asingle accelerometer 12, positioned in any configuration to provideacceleration measurements for use by the filtering element 14 and/orprocessing system 16.

In embodiments including at least two accelerometers 12 as shown in FIG.2, a first one of the accelerometers 12 may measure acceleration along afirst direction a₀ and a second one of the accelerometers 12 may measureacceleration along a second direction a₁. To facilitate the accurateestimation of the motion parameter, direction a₀ may be separated fromdirection a₁ by an angle of between about forty-five degrees andone-hundred thirty-five degrees. In some embodiments, direction a₀ maybe substantially perpendicular to direction a₁ to facilitate theaccurate estimation of motion parameters. In embodiments where theaccelerometers 12 are coupled with a runner's shoe, directions a₀ and a₁may be parallel to the sagittal plane of the subject element S.

In embodiments including at least three accelerometers 12, a first oneof the accelerometers 12 may measure acceleration along a firstdirection a₀, a second one of the accelerometers 12 may measureacceleration along a second direction a₁, and a third one of theaccelerometers 12 may measure acceleration along a third direction a₂ Tofacilitate the accurate estimation of motion parameters, the directionsa₀, a₁, and a₂ may be separated from each other by angles of betweenabout forty-five degrees and one-hundred thirty-five degrees. In someembodiments, each of the directions a₀, a₁, and a₂ may be substantiallymutually perpendicular to each other to facilitate the accurateestimation of motion parameters.

In embodiments including only one of the accelerometers 12, theaccelerometer 12 may be adapted to measure acceleration in a plane ofmotion substantially parallel to the sagittal plane of the subjectelement S. However, the accelerometer 12 may be positioned in anyorientation to measure any acceleration component for use by thefiltering element 14 and/or processing system 16. For instance, inembodiments where the accelerometer 12 is coupled with a runner's shoe,the accelerometer 12 may be configured to measure accelerations at anangle of about forty-five degrees from the plane of the shoe's sole.

The one or more of the accelerometers 12 may be operable to communicatewith other elements of the apparatus 10, or elements external to theapparatus 10, through wired or wireless connections. Thus, theaccelerometers 12 may be coupled with the filtering element 14 and/orprocessing system 16 through wires or the like. One or more of theaccelerometers 12 may also be configured to wirelessly transmit data toother apparatus 10 elements and devices external to the apparatus 10.For instance, one or more the accelerometers 12 may be configured forwireless communication using various RF protocols such as Bluetooth,Zigbee, and/or any other wireless protocols.

The filtering element 14 is operable to couple with the one or moreaccelerometers 12 and filter acceleration measurements and/or signalscorresponding to acceleration measurements. In some embodiments, theapparatus 10 does not include the filtering element 14 and theprocessing system 16 is operable to use unfiltered accelerationmeasurements and corresponding signals. In other embodiments, thefiltering element 14 may be integral with one or more of theaccelerometers 12, the processing system 16, or both the accelerometers12 and the processing system 16. For example, a first portion of thefiltering element 14 may be integral with one or more of theaccelerometers 12 and a second portion of the filtering element 14 maybe integral with the processing system 16. In other embodiments, thefiltering element 14 may be discrete from both the accelerometers 12 andthe processing system 16.

The filtering element 14 may include analog and digital componentsoperable to filter and/or provide other pre-processing functionality tofacilitate the estimation of motion parameters by the processing system16. In various embodiments as shown in FIG. 4, the filtering element 14is operable to filter signals provided by the one or more accelerometers12, or signals derived therefrom, to attenuate perpendicularacceleration, to compensate for gravity, and/or to minimize aliasing.The filtering element 14 may include discrete components for performingeach of these filtering functions or use the same components andhardware for these, and other, filtering functions.

The filtering element 14 may include any analog and digital componentsfor filtering signals and measurements, including passive and activeelectronic components, processors, controllers, programmable logicdevices, digital signal processing elements, combinations thereof, andthe like. In some embodiments, the filtering element 14 may include adigital microcontroller, such as the MSP430F149 microcontrollermanufactured by TEXAS INSTRUMENTS to provide various static and/oradaptive filters. The filtering element 14 may also include ananalog-to-digital converter to convert analog signals provided by theone or more accelerometers 12 to digitize signals for use by theprocessing system 16. The filtering element 14 may also includeconventional pre-sampling filters.

The perpendicular acceleration operable to be generally attenuated bythe filtering element 14 corresponds to acceleration that is generallyperpendicular to the direction of movement of the subject element S. Forexample, in embodiments where the subject element S is a human runner,the perpendicular acceleration generally corresponds to accelerationthat is perpendicular to the average torso direction of the runner. Insome embodiments, the filtering element 14 includes a low-pass filter 18operable to attenuate components of the signals corresponding tomeasured accelerations that represent motion generally perpendicular tothe direction of movement of the subject element S.

In some embodiments, the low-pass filter 18 may be an adaptive filteroperable to employ static and/or varying cut-off frequencies betweenabout 0.5 Hz and 10 Hz. In some embodiments where parameterscorresponding to human strides are estimated, the low-pass filter 18 mayemploy cut-off frequencies between about 1 Hz and 3 Hz. The filteringelement 14 may acquire the cut-off frequency from the processing system16 based on computations performed by the processing system 16corresponding to the particular stride frequency of the subject elementS. The low-pass filter 18 may additionally or alternatively be adaptedto employ a cut-off frequency corresponding to a gait type identified bythe processing system 16.

In other embodiments, the cut-off frequency for the low-pass filter 18may be a static value based upon the typical stride frequency of arunning or walking human. For instance, the cut-off frequency maycorrespond to a frequency between one and two times the typical stridefrequency of a running and/or walking human, such as a static frequencybetween 1 Hz and 3 Hz. Specifically, in some embodiments, the cut-offfrequency may be about 1.45 Hz for walking humans and about 2.1 Hz forjogging humans.

The gravity compensation provided by the filtering element 14 generallycompensates for the constant acceleration provided by gravity that maybe sensed by one or more of the accelerometers 12. In some embodiments,the filtering element 14 includes a high-pass filter 20 operable tofilter or attenuate components of signals corresponding to measuredaccelerations below a given cut-off frequency. The cut-off frequency ofthe high-pass filter 20 may correspond to a frequency approaching 0 Hz,such as 0.1 Hz, to adequately provide compensation for gravity-relatedacceleration.

The anti-aliasing provided by the filtering element 14 generally reducesor prevents aliasing caused by sampling of the signals provided by, orderived from, the one or more accelerometers 12. In some embodiments,the filtering element 14 includes a relatively wideband filter 22designed to attenuate signal frequencies in excess of one-half of thesampling frequency used in any subsequent analog-to-digital conversionsprovided by the processing system 16 or other devices associated withthe apparatus 10. In some embodiments, the filtering element 14 mayprovide other filtering components instead of, or in addition to, thewideband filter 22 to compensate for aliasing. For instance, thefiltering element 14 may include one or more analog and/or digitalfilters to perform any combination of the various filteringfunctionality discussed herein. In some embodiments, a single filteringelement may be utilized to perform each of the filtering functionsdiscussed above such that separate or discrete filters are notnecessarily employed for different filtering functions.

The processing system 16 is generally operable to couple with the one ormore accelerometers 12 and/or the filtering element 14 to generate themotion parameter metric using one or more measured accelerations andestimate the motion parameter based on the generated motion parametermetric. The processing system 16 may include various analog and digitalcomponents operable to perform the various functions discussed herein.In some embodiments, the processing system 16 may include amicroprocessor, a microcontroller, a programmable logic device, digitaland analog logic devices, computing elements such as personal computers,servers, portable computing devices, combinations thereof, and the like.

To facilitate the generation of the motion parameter metric andestimation of the motion parameter, the processing system 16, filteringelement 14, accelerometers 12, and/or other portions of the apparatus 10may limit the dynamic range of acceleration measurements used togenerate the motion parameter metric. For example, accelerationmeasurements outside a specified dynamic range, such as plus or minus 8g, may be saturated at the dynamic range limits to further limit theeffects of perpendicular acceleration. The dynamic range may be variedby the processing system 16 based on the particular motion parameterbeing estimated or according to other sensed or generated measurements.

The processing system 16 may also include, or be operable to couplewith, a memory. The memory may include any computer-readable memory orcombination of computer-readable memories operable to store data for useby the processing system 16. For instance, the memory may be operable tostore acceleration data, motion parameter metric data, statistical data,motion parameter data, filtering data, configuration data, combinationsthereof, and the like.

The processing system 16 may be discrete from the various accelerometers12 and filtering element 14 discussed above. In other embodiments, theprocessing system 16 may be integral with other portions of theapparatus 10. For instance, the same microcontroller or microprocessormay be utilized to implement the filtering element 14 and the processingsystem 16.

In embodiments where the motion parameter to be estimated by theprocessing system 16 corresponds to a stride of a human or animal, theprocessing system 16 may be operable to determine the duration of thestride using measurements provided by the one or more accelerometers 12.For instance, based on various changes in accelerations measured by theone or more accelerometers 12, the processing system 16 may be able todetermine the time at which a stride begins and ends, such as bydetermining when a runner's foot impacts the ground, when a runner'sfoot leaves the ground, when a runner's foot is stationary relative tothe ground, combinations thereof, and the like. Thus, by analyzingvarious changes in measured accelerations, the processing system 16 maycompute the stride duration and information corresponding thereto, suchas stride frequency. The stride frequency may represent the number ofstrides per second or other indications of the rate of stride.

In some embodiments, the processing system 16 may provide the strideduration and/or stride frequency to the filtering element 14 for use indetermining the various cut-off frequencies discussed above. Thus, theprocessing system 16 may dynamically determine the stride duration andstride frequency based on received acceleration measurements and thefiltering element 14 may adapt to provide accurate filtration based onthe particular performance of the subject element S. For example, thefiltering element 14 may filter perpendicular acceleration based on thestride frequency calculated by the processing system 16 to facilitatethe accurate estimation of the motion parameter.

The processing system 16 may additionally or alternatively be operableto determine and/or estimate the gait characteristics of the subjectelement S. By utilizing the accelerations measured by the accelerometers12, the processing system 16 may determine if the subject element S, oranimal or human corresponding to the subject element S, is walking,jogging, running, sprinting, idling, and the like. For instance, theprocessing system 16 may identify rapid and sustained accelerations todetermine that a gait corresponds to running or sprinting. Theprocessing system 16 may also determine, using the acquiredaccelerations, if the gait is efficient, high-impact, irregular,combinations thereof, and the like.

The motion parameter metric generated by the processing system 16 may beany metric that corresponds to the motion parameter to be estimated. Insome embodiments where the motion parameter generally corresponds tostride speed, the motion parameter metric may correspond to themagnitude of the accelerations measured by the one or moreaccelerometers 12 and/or filtered by the filtering element 14.Acceleration magnitude may be defined as:

r ²(t)=a ₀ ²(t)+ . . . +a _(n) ²(t),

where r(t) is the magnitude of the resultant acceleration and a_(n)(t)represents any number of measured and/or filtered accelerations. Anynumber of measured and/or filtered accelerations may be used to computethe acceleration magnitude, including a single acceleration.

In embodiments corresponding to stride speed that utilize accelerationmagnitude, the processing system 16 may compute the motion parametermetric by integrating the acceleration magnitude. For instance, invarious embodiments the motion parameter metric may be given by:

${Q_{0} = {\frac{1}{T}{\int_{0}^{T}{\int_{0}^{t}{{r(\tau)}\ {\tau}\ {t}}}}}},$

where Q₀ is the motion parameter metric, T is the stride durationcalculated by the processing system 16, and r(t) is the accelerationmagnitude calculated by the processing system 16.

The motion parameter metric may additionally or alternatively be givenby any of the following:

${Q_{1} = {\frac{1}{T}{\int_{0}^{T}{\left( \sqrt{\int_{0}^{t}{{r^{2}(\tau)}\ {\tau}}} \right)\ {t}}}}},{Q_{2} = {\frac{1}{T}{\int_{0}^{T}{\left( \sqrt{\int_{0}^{t}{\left( {{r^{2}(\tau)} - g^{2}} \right)\ {\tau}}} \right)\ {t}}}}},\ {Q_{3} = {\frac{1}{T}{\int_{0}^{T}{\int_{0}^{t}{\sqrt{\left( {{r^{2}(\tau)} - g^{2}} \right)}\ {\tau}\ {t}}}}}},{and}$${Q_{4} = {\frac{1}{T}{\int_{0}^{T}{\int_{0}^{t}{\left( {{r(\tau)} - g} \right)\ {\tau}\ {t}}}}}},$

where Q₁ through Q₄ represent various motion parameter metrics, T is thestride duration calculated by the processing system 16, r(t) is theacceleration magnitude calculated by the processing system 16, and g isthe gravitational constant.

In embodiments using metrics Q₁ through Q₄, the subtraction of thegravitational constant g may be used to correct for gravity-relatedacceleration without the use of the high-pass filter 20 discussed above.However, any combination of metrics Q₀ through Q₄ may be employed incombination with the high-pass filter 20 to correct for gravity-relatedacceleration.

Any motion parameter metric may be utilized by the processing system 16and embodiments of the present invention are not limited to theexemplary motion parameter metrics provided above. Further, the utilizedmetric may correspond to other factors acquired or calculated by theprocessing system 16. In some embodiments, the processing system 16 mayselect the metric based on the calculated gait of the subject element Sor human or animal corresponding to the subject element S. For example,if the calculated gait is a walking gait, the processing system 16 mayutilize the metric Q₀ while for other gaits the processing system 16 mayutilize any one of metrics Q₁ through Q₄. Additionally, the processingsystem 16 may calculate a plurality of metrics, such as by using anycombination of metrics Q₀ through Q₄ to facilitate estimation of themotion parameter.

The processing system 16 utilizes the one or more generated metrics toestimate the motion parameter. The estimation performed by theprocessing system 16 generally corresponds to a correlation between themotion parameter metric and motion parameter and is not necessarily adirect computation based on user kinematics. Consequently, theprocessing system 16 may estimate the motion parameter utilizingstatistics and/or other empirical information even when a directcomputation of the motion parameter is difficult or impossible toperform. The estimated motion parameter may correspond to stride speed,acceleration, velocity, stride distance, total distance, gaitefficiency, power, energy, maximum impact, average calories consumed,maximum speed change, speed variability, combinations thereof, and thelike. However, the estimated motion parameter may correspond to anyparameter associated with the motion of the subject element S.

In some embodiments, the processing system 16 may estimate the motionparameter using the generated motion parameter metric and a statisticalmodel. The statistical model may be a regression model selected from thegroup consisting of a linear regression model, a polynomial regressionmodel, a multiple-regression model, a piecewise-linear regression model,combinations thereof, and the like.

For instance, the processing system 16 may correlate the generatedmotion parameter metric to stride speed as shown in the regression modelof FIG. 11. In some embodiments, the processing system 16 may utilize adatabase, a look-up table, or other information stored within its memoryto estimate the motion parameter using the motion parameter metric andthe statistical model. For example, given a particular motion parametermetric, the processing system 16 may access the memory to acquire acorresponding motion parameter. Thus, in some embodiments thestatistical model may comprise a database of information not limited toany particular regression model. As is discussed in more detail below,the processing system 16 may be operable to correct and/or adjust thestatistical model using truth measurements and information provided fromother sources.

The processing system 16 may also use a plurality of statistical modelsto estimate one or more motion parameters. In some embodiments, theprocessing system 16 may be configured to select which one or more ofthe statistical models may be used to estimate the motion parameter. Forexample, the processing system 16 may use information specific to thesubject element S, such as age, gender, weight, height, configuration,shape, and the like, to select one or more of the statistical models.The processing system 16 may also select statistical models based on theconfiguration of the apparatus 10, such as the position of the one ormore accelerometers 12 on the subject element S, the number and type ofaccelerometers 12 utilized, the number of acceleration measurementsreceived, combinations thereof, and the like.

In various embodiments, the processing system 16 is operable to computethe motion parameter metric and/or estimate the motion parameter foreach detected stride to facilitate the accurate analysis of movement.Thus, for every stride detected as discussed above, or for anycombination of strides, the computing element may estimate the motionparameter. Further, in some embodiments, the processing system 16 mayestimate the motion parameter using metrics corresponding to a pluralityof strides. For example, the estimated motion parameter may correspondto a total or average stride speed resulting from several strides.

In some embodiments, each generated motion parameter metric and/orestimated motion parameter may be stored in the memory associated withthe processing system 16, or in any other computer-readable memory, toallow later analysis by the processing system 16 or other devicesassociated therewith. The stored information may be time-correlated tofacilitate analysis and compressed to reduce the required capacity ofthe memory.

The processing system 16 may additionally or alternatively utilizeinformation acquired from sensors other than the one or moreaccelerometers 12. For instance, in some embodiments the processingsystem 16 may couple with a heart rate monitor, acquire heart rateinformation from the heart rate monitor, and generate the motionparameter metric and/or estimate the motion parameter using the heartrate information and/or acceleration measurements. Similarly, theprocessing system 16 may couple with other sensors to acquirenon-acceleration kinematic variables such as velocity and/orenvironmental variables such as ambient temperature and altitude. Forexample, to acquire additional information, the processing system 16 maycouple with, and/or include, radio-frequency transceivers, thermometers,altimeters, compasses, inclinometers, pressure sensors, blood pressuremonitors, light sensors, atmospheric sensors, angular velocity sensorsand other inertial sensors, microphones, computing devices such aspersonal computers, cellular phones, and personal digital assistances,other similarly configured apparatuses, combinations thereof, and thelike.

In some embodiments, as shown in FIGS. 6 through 9, the apparatus 10 maybe operable to receive information from at least one navigation device24. The navigation device 24 may be adapted to provide geographiclocation information to the apparatus 10 and users of the apparatus 10.The navigation device 24 may include a GPS receiver much like thosedisclosed in U.S. Pat. No. 6,434,485, which is incorporated herein byspecific reference. However, the navigation device 24 may use cellularor other positioning signals instead of, or in addition to, the GPS tofacilitate determination of geographic locations. The navigation device24 may be operable to generate navigation information such as the speedof the navigation device 24, the current and previous locations of thenavigation device 24, the bearing and heading of the navigation device24, the altitude of the navigation device 24, combinations thereof, andthe like.

The processing system 16 may use the information received from thenavigation device 24 to generate the motion parameter metric and/or toestimate the motion parameter. The processing system 16 may also use andpresent acquired navigation information independent of the metrics andestimated parameters. Additionally or alternatively, the processingsystem 16 may use the information acquired from the navigation device 24to correct and/or adjust the statistical model. For instance, theprocessing system 16 may compare distances and speeds generated fromaccelerations provided by the one or more accelerometers 12 withdistances and speeds provided by the navigation device 24 and correctthe statistical model to enable distances and speeds generated frommeasured accelerations to be as accurate as those provided by thenavigation device 24. Thus, the processing system 16 may be periodicallycoupled with the navigation device 24 to correct the statistical modelto ensure that the apparatus 10 accurately estimates motion parameterseven when not coupled with the navigation device 24.

The filtering element 14 and processing system 16 may additionally beoperable to compensate for part-to-part manufacturing variabilitypresent in the one or more accelerometers 12, including characterizationover temperature of zero-g bias point, sensitivity, cross-axissensitivity, nonlinearity, output impedance, combinations thereof, andthe like.

The apparatus 10 may also utilize information acquired from thenavigation device 24 to form an error model operable to be used incombination with the statistical model to increase the accuracy of theestimated motion parameters. The error model can, for example, be linearas a function of speed. Furthermore, the error model can includedependence on other factors such as stride cadence, gait type, elevationchange, gait efficiency or other gait characteristics.

Further, in some embodiments the apparatus 10 may utilize auser-calibration sequence where one or more motion parameters estimatedby the apparatus 10 are compared to the true values of the parameters assupplied by a user. The user-calibration sequence may be used to furtheradjust the statistical model and/or motion parameter metrics to refinethe accuracy of subsequent motion parameter estimations.

In some embodiments, as shown in FIG. 5, the apparatus 10 may include acommunications element 26 to enable the apparatus 10 to communicate withother computing devices, exercise devices, navigation devices, sensors,and any other enabled devices through a communication network, such asthe Internet, a local area network, a wide area network, an ad hoc orpeer to peer network, combination thereof, and the like. Similarly, thecommunications element 26 may be configured to allow directcommunication between similarly configured apparatuses using USB,Bluetooth, Zigbee, Firewire, and other connections, such that theapparatus 10 need not necessarily utilize a communications network toacquire and exchange information.

In various embodiments the communications element 26 may enable theapparatus 10 to wirelessly communicate with communications networksutilizing wireless data transfer methods such as WiFi (802.11), Wi-Max,Bluetooth, ultra-wideband, infrared, cellular telephony, radiofrequency, and the like. However, the communications element 26 maycouple with the communications network utilizing wired connections, suchas an Ethernet cable, and is not limited to wireless methods.

The communications element 26 may be configured to enable the apparatus10 to exchange data with external computing devices to facilitate thegeneration and/or analysis of information. For example, the processingsystem 16 may use information acquired through the communicationselement 26 in generating the motion parameter metrics, estimating motionparameters, and correcting statistical models. The processing system 16may also provide generated motion parameter metrics and estimated motionparameters through the communications element 26 for use by externaldevices. For instance, the external devices can be configured to store,analyze, and exchange information between a plurality of users and/or aplurality of devices attached to one or multiple users.

Consequently, the communications element 26 generally enables real-timecomparison of information generated by the apparatus 10 and otherdevices. The communications element 26 also enables the apparatus 10 tostore data on one or more of the external devices for later retrieval,analysis, aggregation, and the like. The data can be used byindividuals, their trainers or others to capture history, evaluateperformance, modify training programs, compare against otherindividuals, and the like. The data can also be used in aggregated form.

The apparatus 10 may additionally include a user interface 28 to enableusers to access various information generated and acquired by theapparatus 10, such as acceleration measurements, motion parametermetrics, estimated motion parameters, navigation information acquiredfrom the navigation device 24, information and data acquired through thecommunications element 26, configuration information, combinationsthereof, and the like. The user interface 28 facilities, for example,powering on/off the apparatus 10, selecting which content to display,and providing configuration information such as the attributes of thesubject element S.

The user interface 28 may include one or more displays to visuallypresent information for consumption by users and one or more speakers toaudibly present information to users. The user interface 28 may alsoinclude mechanical elements, such as buzzers and vibrators, to notifyusers of events through mechanical agitation. In some embodiments, asshown in FIG. 1, the user interface 28 may be implemented within a watchoperable to be worn on a user's wrist, forearm, and/or arm. Thus, theuser interface 28 may be positioned separately from one or more of theaccelerometers 12 to enable the user to easily interact with theapparatus 10. However, in some embodiments the user interface 28 andaccelerometers 12 may be integral.

The user interface 28 may also be operable to receive inputs from theuser to control the functionality of the processing system 16 and/ordevices and elements associated therewith. The user interface 28 mayinclude various functionable inputs such as switches and buttons, atouch-screen display, optical sensors, magnetic sensors, thermalsensors, inertial sensors, a microphone and voice-recognitioncapabilities, combinations thereof, and the like. The user interface 28may also include various processing and memory devices to facilitate itsfunctionality.

In some embodiments, the user interface 28 may be able to detect themotion of the user interface unit 30, such as by using accelerationssensed by the one or more accelerometers 12 or additional inertialsensors such as one or more accelerometers associated with the userinterface 28, to identify user inputs. For example, the user interface28 may detect if the user gestures in a particular manner, such as bytapping the user interface unit 30 or other portions of the userinterface 28, or by waving his or her hand, and acquire a correspondinguser input. Thus, instead of functioning a button or a conventionaltouch-screen display the user may gesture in a particular manner tocontrol the functionality of the apparatus 10.

In some embodiments, the user interface 28 does not include some or allof the buttons or the like, and the user input normally associated withthe buttons is acquired by using inertial sensors and specific motions.For example, the user interface 28 may be operable to acquire userinputs by sensing taps on the user interface 28 and/or user interfaceunit 30 and estimating the location, direction, strength, count and/orfrequency of the taps. For instance, a double tap approximately on theright side of a display associated with the user interface 28 mayindicate “select”, while a single tap near the top of the display mayindicate “scroll up”. Consequently, by tapping on the display or otherportions of the user interface unit 30, the user may easily provideinputs to the apparatus 10 without functioning buttons or the like. Theuser interface 28 may also be operable to acquire user inputs based onthe orientation of the apparatus 10. For instance, the one or moreaccelerometers 12 and/or other accelerometers associated with the userinterface 28 may be utilized to detect the orientation of the apparatus10 based on the acceleration and force provided by gravity.

The user interface 28 enables users to receive real-time feedbackconcerning the estimated motion parameter and associated information.For instance, the user interface 28 may present the currently estimatedmotion parameter, such as a current stride speed and distance, and/orinformation associated therewith or with other motion parameters, suchas total distance, calories expended, total speed, combinations thereof,and the like.

Utilizing the communications element 26, the user interface 28 alsoenables users to receive real-time feedback and comparisons with otherusers and devices. For instance, as shown in FIG. 10, a plurality ofapparatuses 10 may be employed by a plurality of runners to enable data,metrics, and parameters corresponding to each runner to be shared andpresented to the user. Thus, for instance, the user may ascertain thespeed and location of other users through the user interface 28.

Further, the user interface 28 may acquire comparison information fromthe processing system 16 and/or from other devices through thecommunications element 26 to enable the user to compare his or herperformance using the comparison information. For instance, the userinterface 28 may present a comparison of the user's current performancewith a previous performance by the user, with a training model, and/orwith another individual.

In various embodiments, the user may configure the apparatus 10utilizing the user interface 28 to monitor estimated motion parametersand alert the user through the user interface 28 when one or moreestimated motion parameters conflict with a user-defined condition suchas an acceptable parameter range, threshold, and/or variance. The usermay also configure the apparatus 10 utilizing the user interface 28 tomonitor various user-defined goals, such as time limits, motionparameter maximum values, and the like. The apparatus 10 mayadditionally monitor the estimated motion parameter metrics to determineif the subject element S is in danger and, if necessary, request helpusing the communications element 26. To facilitate motion parametermonitoring, the user may provide the processing system 16 with variousactivity programs that may be imported utilizing the communicationselement 26 or defined through the user interface 28.

As is discussed above, the various components of the apparatus 10 may behoused integrally or separately in any combination. In some embodiments,the apparatus 10 includes an interface unit 30 for housing the userinterface 28 and associated components and a sensor unit 32 for housingthe one or more accelerometers 12, the processing system 16, and thecommunications element 26. In some embodiments, the units 30, 32 may behoused within the same housing, as is shown in FIG. 9. However, in otherembodiments the units 30, 32 may be discrete such that the sensor unit32 may be positioned in a first location, such as on the user's shoe,and the interface unit 30 may be positioned at a second location, suchas on the user's wrist.

The interface unit 30 may also include an interface communicationelement 34, configured in a similar manner to the communications element26 discussed above, to enable the interface unit 30 to exchangeinformation with the sensor unit 32, other parts of the apparatus 10,and/or with devices external to the apparatus 10. In embodiments wherethe units 30, 32 are positioned separate from each other, thecommunications elements 26, 34 may communicate utilizing the variouswireless methods discussed above. However, the communications elements26, 34 may also communicate utilizing wired connections or throughexternal devices and systems.

The units 30, 32 may also each include power sources for powering thevarious components of the apparatus 10, such as through the use ofbatteries or power-generating elements such as piezoelectric,electromechanical, thermoelectric, and photoelectric elements. In someembodiments, portions of the user interface 28 may be included with bothunits 30, 32 such that each unit 30, 32 and its respective componentscan be individually functioned by the user.

As shown in FIG. 5, the apparatus 10 may additionally include anexternal systems unit 36 to enable the interface unit 30 and sensor unit32 to easily communicate with external systems and devices. For example,the external systems unit 36 may include a communications element tocommunicate with the other communication elements 26, 34, amicrocontroller to process information, and a standard interface such asa WiFi, Bluetooth, USB, or ZigBee interface operable to easily interfacewith devices such as cellular phones, portable media players, personaldigital assistants, navigation devices, personal and portable computingdevices, combinations thereof, and the like. Thus, in some embodiments,the external systems unit 36 may be connected with an immobile personalcomputer and the interface unit 30 and sensor unit 32 may be positionedon a mobile user, as is shown in FIG. 10.

As is shown in FIGS. 6 through 9, the interface unit 30 and sensor unit32 may each be operable to communicate with the navigation unit 24 toreceive and utilize navigation information. The navigation device 24 maybe discrete from the units 30, 32, as shown in FIG. 6, the navigationdevice 24 may be integral with the interface unit 30, as shown in FIG.7, the navigation device 24 may be integral with the sensor unit 32, asshown in FIG. 8, and/or the navigation device 24 may be integral withboth units 30, 32, as shown in FIG. 9. Further, in some embodiments, anyone or more of the units 30, 32, 36 and navigation device 24 may beautomatically disabled when not in use to achieve optimum system powerconsumption and functionality.

In some embodiments, the sensor unit 32 may be attached to the user'swrist in an enclosure which is similar to a watch and combined withother functionality such as timekeeping or with other sensors such thenavigation device 24. In other embodiments, the sensor unit 32 may beattached to the user's arm using an enclosure similar to an armband andcombined with other devices such as a cellular phone, an audio deviceand/or the navigation device 24. In various other embodiments, thesensor unit 32 may be attached to the user with a chest strap in anenclosure which may include other sensors such as a heart-rate monitor(HRM). In yet other embodiments, the sensor unit 32 may be attached touser's waist with, for example, a belt clip. In other embodiments, thesensor unit 32 may be configured to identify its position on the user'sbody, thereby allowing the user to carry or attach the sensors in anarbitrary location on his or her body such as in a pocket, and/orcombined with another device such as a cellular phone.

Consequently, the apparatus 10 is operable to estimate motion parametersusing only acceleration measurements acquired from the one or moreaccelerometers 12, using acceleration measurements in combination withother information acquired from the navigation unit 24 or other devicesthrough the communications element 26, using information other thanacceleration measurements, combinations thereof, and the like.

The processing system 16 may additionally monitor the activity of thesubject element S utilizing the acceleration measurements, motionparameter metric, and/or estimated motion parameter. In someembodiments, the processing system 16 may utilize accelerationmeasurements and/or other information to identify the type of activitythat the subject element S is engaging in and automatically provideappropriate content based upon the identified activity without requiringuser input. For example, if the user switches from walking to jogging,the processing system 16 may identify the change, computejogging-related metrics and motion parameters, and displayjogging-related information using the user interface 28. As anotherexample, the processing system 16 may identify that the user is swimmingbased upon the acceleration measurements and generate and displayswimming-related information such as cadence, stroke power, lap times,and the like.

Other activities which can, for example, be classified or otherwiseidentified by the processing system 16 include: walking; running;swimming; racquet sports; shuffling; driving; exercising on a stationarybicycle or apparatus; hiking; rollerblading; skateboarding; low-energyactivities such as office activities and watching television; sleeping;dancing; playing sports such as basketball, football or golf;combinations thereof; and the like. Thus, the apparatus 10 mayautomatically provide information for a plurality of activities withoutrequiring manual reconfiguration or programming by the user.

In some embodiments, the processing system 16 may be configured toutilize a multi-resolution approach in storing information and datacorresponding to sensed measurements and activities. For example, at thelowest resolution, the time, date, classification, duration and totalenergy expenditure of each activity may be saved. Another resolution mayallow data to be stored corresponding to, for example, for jogging, theaverage pace, average cadence, total distance, total elevation change,and the like. Another resolution may allow data to be storedcorresponding to, again for jogging, for example, individual strideparameters and/or frequent measurements of heart rate, elevation, pace,and/or associated GPS coordinates. The history resolution depth for eachtype of activity can be pre-selected by the user or be automaticallyselected by the processing system 16 based on the amount of storagespace available. In some embodiments, all activities are initiallyrecorded at the highest available resolution; subsequently, if storagespace becomes a constraint, highest resolution records of oldestactivities may be erased to allow for storage of the most recentactivities at a history resolution at least as good as resolution of theoldest records.

Further, the processing system 16 may provide context-awarefunctionality utilizing measured accelerations, motion parametermetrics, estimated motion parameters, information acquired through theuser interface 28, information acquired through communications element26 or other devices such as the navigation device 24, combinationsthereof, and the like. For example, the processing system 16 may detect:if the apparatus 10 is being used to estimate motion parameters ormonitor user performance; if the apparatus 10 is not being used; if theapparatus 10 is being charged; if the apparatus 10 is in proximity to acompatible external system or device; if the apparatus 10 is inproximity to a display device such as a cellular phone, personal digitalassistant, computer, audio device, heads-up display, watch; combinationsthereof; and the like.

Based on the determination of the use context and with minimal or nouser intervention, the apparatus 10 can provide any appropriate set offunctions. For example, while in proximity to a compatible externalsystem, the apparatus 10 can automatically establish a communicationchannel and exchange information with the compatible external system.Similarly, while monitoring user activity, the apparatus 10 can recordmotion history and associated motion parameters and metrics. While notin use, the apparatus 10 can disable most of its sensors to conserveenergy and enable a subset of the sensors, such as the one or moreaccelerometers 12, only frequently enough to maintain context awareness.While in proximity to a display device, the apparatus 10 can determinethe capabilities of the device, and communicate appropriate informationto the display device. The use contexts are not necessarily mutuallyexclusive. For example, the apparatus 10 can be charging and be inproximity to a compatible external system at the same time. Thus, whilecharging, the apparatus 10 can continue the sensing of nearby compatibleexternal systems and, upon detection of a compatible external system,establish a communication channel and exchange information with thecompatible external system. The user thus perceives and expects theapparatus 10 to be always enabled and the apparatus 10 requires minimalor no user input to perform all of its functions.

The activity monitoring and/or context awareness discussed above may beutilized by the apparatus 10 to maintain a generally continuous recordof the user's activities. For example, the user may wear the apparatus10 continuously or repeatedly to monitor long-term activity, such astrends, goals, and the like. Generally continuous monitoring of useractivity by the apparatus 10 also enables alerts to be issued if theprocessing system 16 detects abnormal activity. For example, if the userremains generally immobile for extended periods of time, the processingsystem 16 may issue an alert to notify the user through the userinterface 28 and/or alert third-parties utilizing the communicationselement 26.

It is believed that embodiments of the present invention and many of itsattendant advantages will be understood by the foregoing description,and it will be apparent that various changes may be made in the form,construction and arrangement of the components thereof without departingfrom the scope and spirit of the invention or without sacrificing all ofits material advantages. The form herein before described being merelyan explanatory embodiment thereof, it is the intention of the followingclaims to encompass and include such changes.

1. A method of estimating a motion parameter corresponding to a subjectelement, the method comprising: acquiring an acceleration measurement;generating a motion parameter metric utilizing the accelerationmeasurement; and estimating the motion parameter using the motionparameter metric.
 2. The method as claimed in claim 1, wherein themotion parameter is estimated utilizing the motion parameter metric anda statistical model.
 3. The method as claimed in claim 2, wherein thestatistical model is a regression model selected from the groupconsisting of a linear regression model, a polynomial regression model,a multiple-regression model, a piecewise-linear regression model, andcombinations thereof.
 4. The method as claimed in claim 1, furtherincluding acquiring a plurality of acceleration measurements andgenerating the motion parameter metric utilizing the accelerationmeasurements.
 5. The method as claimed in claim 4, further includingfiltering the acceleration measurements for use in generating the motionparameter metric.
 6. The method as claimed in claim 5, wherein theacceleration measurements are low-pass filtered utilizing a cut-offfrequency between about 0.5 Hz and 10 Hz.
 7. The method as claimed inclaim 6, wherein the cut-off frequency is generated using a stridefrequency calculated from the acceleration measurements.
 8. The methodas claimed in claim 1, wherein the estimated motion parameter isselected from the group consisting of stride speed, acceleration,velocity, stride distance, total distance, gait efficiency, power,energy, maximum impact, average calories consumed, maximum speed change,speed variability, and combinations thereof.
 9. The method as claimed inclaim 1, further including acquiring a stride duration and generatingthe motion parameter metric utilizing the acceleration measurement andthe stride duration.
 10. The method as claimed in claim 1, furtherincluding obtaining an acceleration magnitude and generating the motionparameter metric utilizing the acceleration measurement and theacceleration magnitude.
 11. The method as claimed in claim 10, whereinthe motion parameter corresponds to a stride speed and the motionparameter metric is at least partially generated by performing anintegration of a function of the acceleration magnitude.
 12. Anapparatus operable to estimate a motion parameter corresponding to asubject element, the apparatus comprising: an accelerometer operable toprovide a signal corresponding to an acceleration measurement; and aprocessing system coupled with the accelerometer and operable to:acquire the signal corresponding to the acceleration measurement,generate a motion parameter metric utilizing the accelerationmeasurement, and estimate the motion parameter using the motionparameter metric and a statistical model.
 13. The apparatus as claimedin claim 12, further including a plurality of accelerometers operable toprovide a plurality of signals corresponding to accelerationmeasurements, the processing system being operable to acquire thesignals and generate the motion parameter metric utilizing theacceleration measurements.
 14. The apparatus as claimed in claim 13,wherein at least two of the accelerometers are adapted to measureaccelerations in two directions separated by an angle greater than zerodegrees.
 15. The apparatus as claimed in claim 14, wherein the apparatusincludes at least three accelerometers adapted to measure accelerationsin three directions each separated by an angle greater than zerodegrees.
 16. The apparatus as claimed in claim 13, further including afilter operable to filter the signals corresponding to accelerationmeasurements for use in generating the motion parameter metric.
 17. Theapparatus as claimed in claim 16, wherein the filter includes a low-passfilter operable to utilize a cut-off frequency between about 0.5 Hz and10 Hz
 18. The apparatus as claimed in claim 17, wherein the processingsystem is further operable to calculate a stride frequency using theacceleration measurements and generate the cut-off frequency using thestride frequency.
 19. The apparatus as claimed in claim 12, wherein theestimated motion parameter is selected from the group consisting ofstride speed, acceleration, velocity, stride distance, total distance,gait efficiency, power, energy, maximum impact, average caloriesconsumed, maximum speed change, speed variability, and combinationsthereof.
 20. The apparatus as claimed in claim 12, wherein theprocessing system is further operable to calculate a stride durationusing the acceleration measurement and generate the motion parametermetric using the acceleration measurement and the stride duration. 21.The apparatus as claimed in claim 12, wherein the processing system isfurther operable to obtain an acceleration magnitude and generate themotion parameter metric utilizing the acceleration measurement and theacceleration magnitude.
 22. The apparatus as claimed in claim 21,wherein the motion parameter corresponds to a stride speed and themotion parameter metric is at least partially generated by performing anintegration of a function of the acceleration magnitude.
 23. Theapparatus as claimed in claim 12, wherein the statistical model is aregression model selected from the group consisting of a linearregression model, a polynomial regression model, a multiple-regressionmodel, a piecewise-linear regression model, and combinations thereof.24. An apparatus operable to estimate a motion parameter correspondingto a subject element, the apparatus comprising: a plurality ofaccelerometers each operable to provide a signal corresponding to anacceleration measurement, at least two of the accelerometers beingadapted to measure accelerations in two directions separated by an anglegreater than zero degrees; and a processing system coupled with one ormore of the accelerometers, the processing system operable to: calculatea stride duration using at least one of the acceleration measurements,low-pass filter the signals corresponding to the accelerationmeasurements utilizing a cut-off frequency between about 0.5 Hz and 10Hz, calculate an acceleration magnitude using the accelerationmeasurements corresponding to the filtered signals, generate a motionparameter metric utilizing the stride duration and accelerationmagnitude, and estimate the motion parameter using the motion parametermetric and a statistical regression model.
 25. The apparatus as claimedin claim 24, wherein the motion parameter corresponds to a stride speedand the motion parameter metric is at least partially generated byperforming an integration of a function of the acceleration magnitude.26. The apparatus as claimed in claim 24, wherein the apparatus includesat least three accelerometers adapted to measure accelerations in threedirections each separated by an angle greater than zero degrees.
 27. Theapparatus as claimed in claim 24, wherein the processing system isfurther operable to limit acceleration measurements to within a specificdynamic range.